Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product

Abstract

Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

“…However, in terms of the mapping of vegetation physiognomic types, poor performance of the MCD12Q1 product has been reported in Japan [35]. On the other hand, visual interpretation techniques have been used for the nationwide vegetation mappings.…”

“…Roy et al [45] used on-screen visual screen technique for the preparation of land use and land cover database in India using medium-resolution Indian remote sensing satellite data. More recently, Sharma et al [35] employed machine learning and automated classification approach for the production of nationwide vegetation physiognomic map in Japan using MODIS data. However, mapping of the vegetation physiognomic types by using the 500 m resolution MODIS datasets are affected by mixed pixel effect, and the resulting map misses distribution of many vegetation types that occurred in smaller patches.…”

“…The ground truth polygon data prepared in the previous studies [7,35] which were basically constructed through on-site field inspection were used in the research. This data comprise of 390 polygons for each class under the study.…”

“…Major techniques used for the detection, classification, and mapping of vegetation using remote sensing imagery are vegetation indices [20,21], spectral mixture analysis [22], temporal image-fusion [23,24], texture based measures [25], and supervised classification using machine learning classifiers such as maximum likelihood [26], random forests [27,28], decision trees [29], support vector machines [30], fuzzy learning [31], and neural networks [32][33][34]. Nevertheless, performance of existing large-scale land cover maps is limited to the discrimination of vegetation physiognomic types, which is still a challenging field [35].…”

Characterization of Vegetation Physiognomic Types Using Bidirectional Reflectance Data

“…For example, extant maps such as MODIS Land Cover Type Product (MCD12Q1, [26]) and Global Land Cover by National Mapping Organizations (GLCNMO, [27]), from which the vegetation physiognomic information can be obtained, have not correctly classified the vegetation physiognomic types over a region as large and diverse as all of Japan [28,29]. With a focus on ground truth data and mapping at the national scale, more accurate vegetation physiognomic maps have been produced in Japan [28,29]. The importance of input features and the size of ground truth data for the classification of vegetation physiognomic types have also been emphasized [30].…”